Computer Simulation of Porous Materials covers the key approaches in the modelling of porous materials, with a focus on how these can be used for structure prediction and to either rationalise or predict a range of properties including sorption, diffusion, mechanical, spectroscopic and catalytic. The book covers the full breadth of (micro)porous materials, from inorganic (zeolites), to organic including porous polymers and porous molecular materials, and hybrid materials (metal-organic frameworks). Through chapters focusing on techniques for specific types of applications and properties, the book outlines the challenges and opportunities in applying approaches and methods to different classes of systems, including a discussion of high-throughput screening. There is a strong forward-looking focus, to identify where increased computer power or artificial intelligence techniques such as machine learning have the potential to open up new avenues of research. Edited by a world leader in the field, this title provides a valuable resource for not only computational researchers, but also gives an overview for experimental researchers. It is presented at a level accessible to advanced undergraduates, postgraduates and researchers wishing to learn more about the topic.

Computer Simulation of Porous Materials covers the key approaches in the modelling of porous materials, with a focus on how these can be used for structure prediction and to either rationalise or predict a range of properties including sorption, diffusion, mechanical, spectroscopic and catalytic. The book covers the full breadth of (micro)porous materials, from inorganic (zeolites), to organic including porous polymers and porous molecular materials, and hybrid materials (metal-organic frameworks). Through chapters focusing on techniques for specific types of applications and properties, the book outlines the challenges and opportunities in applying approaches and methods to different classes of systems, including a discussion of high-throughput screening. There is a strong forward-looking focus, to identify where increased computer power or artificial intelligence techniques such as machine learning have the potential to open up new avenues of research. Edited by a world leader in the field, this title provides a valuable resource for not only computational researchers, but also gives an overview for experimental researchers. It is presented at a level accessible to advanced undergraduates, postgraduates and researchers wishing to learn more about the topic.